1. Field of the Invention
The invention lies in the field of electronic communications. The invention relates to a method and device for adaptive quantization of soft bits.
2. Description of the Related Art
Error correction coding is a general technique of digital communication systems to improve the power efficiency at the cost of the bandwidth efficiency. The performance of the error correction coding can be improved even more at a cost of the reasonable complexity when a soft decision technique is exploited. In comparison to a general hard decision technique that detects a received bit as a hard bit (which has only two levels 0 and 1), the soft decision technique detects a bit as a soft bit, which has more than 2 levels, for example, from 0 to 15 for a four-bit digital number. In this 16-level example, the level 15 of the soft bit is a very strong 1 and the level 8 of the soft bit is a very weak 1. Similarly, the level 7 of the soft bit is a very weak 0 and the level 0 of the soft bit is a very strong 0. These soft bits give more detailed information of the transmitting data, even though the channel distorts the data, than the less detailed information provided by the general hard bits with the same channel distortion. Accordingly, the soft decoding scheme of a coding scheme gives better error correction capability than the general hard decoding scheme.
The general procedure of a soft decision technique with a modulation such as PSK and QAM is set forth in FIG. 1. The PSK or QAM symbol of a single-carrier or a multi-carrier signal is received as a complex number Xr. The received symbol is demodulated to true soft bits, which are real numbers, by a log likelihood ratio (“LLR”) calculation. The true soft bit, that is, the LLR of a soft bit, shows the degree of 0 or 1 of a received bit. A large positive number means the received bit is a strong 1 and a large negative number means that the received bit is a strong 0. In comparison, the small positive number means the received bit is a weak 1 and the small negative number means the received bit is a weak 0. To apply a soft detector in a real implementation, however, this continuous soft bit should be quantized to a discrete soft bit.
A significant goal of such a quantization procedure should be to maximize the decoder performance. Generally, quantization to a discrete soft bit is accomplished, in the prior art, with a linear quantization process, which determines the quantization range from −a to +b (b can be equal to a) and divides the range evenly to the more than 2 levels (16 levels in the above example). The LLR having a value smaller than −a is assigned to the first level (level 0 in the 16-level example) and the LLR having a value larger than +b is assigned to the last level (i.e., level 15). The quantized soft bits, then, become the input for a soft decoder, which decodes the coded soft bits to the decoded binary bits. This process is illustrated in FIG. 1.
In a linear quantization, each measurement within the entire range of the distribution is assigned to one of the levels, for example, to one of the sixteen levels. Significant, however, is the fact that for each measurement the quantization range is changing. The change occurs in the range because, for every block, the LLR distribution is changing. The change of the LLR distribution shape is caused by the different fading channels and because of the variance of the distribution due to the change of the ratio of energy per symbol (Es) to noise density (No), also referred to as “Es/No.”
Determination of the quantization range is very important because the bit error rate (“BER”) performance of the communication system is improved when the quantization range is determined properly. However, a significant amount of information, such as the estimated noise level, is required to decide the right quantization level because the LLR distribution changes rapidly according to the signal power and the current channel characteristics. For example, in a static channel, if the signal power is very strong, then the distribution looks like a two narrow Gaussian distribution centered at a positive number and a negative number with each of the distributions having similar amplitude. In contrast, if the signal power is very low, then the distribution looks like a one Gaussian distribution centered at zero.
Two significant problems arise with respect to linear quantization of LLR. First, there is a problem of estimating an Es/No that is always changing, and, second, there is a problem of determining an appropriate quantization range of LLR when the range is always changing.